In a recent survey, Bengio et al. [1]} identified three broad approaches to leveraging machine learning for combinatorial optimization problems:
learning alongside optimization algorithms [2]}, [3]}, [4]},
learning to configure optimization algorithms [5]}, [6]}, [7]},
and end-to-end learning to approximately solve optimization problems, a.k.a. neural combinatorial optimization [8]}, [9]}.
